Overview

Dataset statistics

Number of variables18
Number of observations63
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory146.0 B

Variable types

Categorical1
Numeric17

Alerts

Sector has a high cardinality: 63 distinct valuesHigh cardinality
2000-01 is highly overall correlated with 2001-02 and 15 other fieldsHigh correlation
2001-02 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2002-03 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2003-04 is highly overall correlated with 2000-01 and 15 other fieldsHigh correlation
2004-05 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2005-06 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2006-07 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2007-08 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2008-09 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2009-10 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2010-11 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2011-12 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2012-13 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2013-14 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2014-15 is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
2015-16 is highly overall correlated with 2001-02 and 15 other fieldsHigh correlation
2016-17 is highly overall correlated with 2000-01 and 15 other fieldsHigh correlation
Sector is highly overall correlated with 2000-01 and 16 other fieldsHigh correlation
Sector is uniformly distributedUniform
Sector has unique valuesUnique
2000-01 has 23 (36.5%) zerosZeros
2001-02 has 18 (28.6%) zerosZeros
2002-03 has 13 (20.6%) zerosZeros
2003-04 has 10 (15.9%) zerosZeros
2004-05 has 6 (9.5%) zerosZeros
2005-06 has 7 (11.1%) zerosZeros
2006-07 has 8 (12.7%) zerosZeros
2007-08 has 2 (3.2%) zerosZeros
2008-09 has 5 (7.9%) zerosZeros
2009-10 has 5 (7.9%) zerosZeros
2010-11 has 3 (4.8%) zerosZeros
2011-12 has 4 (6.3%) zerosZeros
2012-13 has 5 (7.9%) zerosZeros
2013-14 has 3 (4.8%) zerosZeros
2014-15 has 2 (3.2%) zerosZeros
2015-16 has 5 (7.9%) zerosZeros
2016-17 has 6 (9.5%) zerosZeros

Reproduction

Analysis started2024-03-14 06:08:09.035724
Analysis finished2024-03-14 06:08:46.675350
Duration37.64 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Sector
Categorical

HIGH CARDINALITY  HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size632.0 B
METALLURGICAL INDUSTRIES
 
1
DEFENCE INDUSTRIES
 
1
PAPER AND PULP (INCLUDING PAPER PRODUCTS)
 
1
SUGAR
 
1
FERMENTATION INDUSTRIES
 
1
Other values (58)
58 

Length

Max length115
Median length37
Mean length26.428571
Min length4

Characters and Unicode

Total characters1665
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)100.0%

Sample

1st rowMETALLURGICAL INDUSTRIES
2nd rowMINING
3rd rowPOWER
4th rowNON-CONVENTIONAL ENERGY
5th rowCOAL PRODUCTION

Common Values

ValueCountFrequency (%)
METALLURGICAL INDUSTRIES 1
 
1.6%
DEFENCE INDUSTRIES 1
 
1.6%
PAPER AND PULP (INCLUDING PAPER PRODUCTS) 1
 
1.6%
SUGAR 1
 
1.6%
FERMENTATION INDUSTRIES 1
 
1.6%
FOOD PROCESSING INDUSTRIES 1
 
1.6%
VEGETABLE OILS AND VANASPATI 1
 
1.6%
SOAPS, COSMETICS & TOILET PREPARATIONS 1
 
1.6%
RUBBER GOODS 1
 
1.6%
LEATHER,LEATHER GOODS AND PICKERS 1
 
1.6%
Other values (53) 53
84.1%

Length

2024-03-14T11:38:46.854384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 12
 
6.2%
11
 
5.7%
industries 6
 
3.1%
including 5
 
2.6%
instruments 3
 
1.6%
other 3
 
1.6%
paper 3
 
1.6%
machinery 3
 
1.6%
products 3
 
1.6%
services 3
 
1.6%
Other values (124) 141
73.1%

Most occurring characters

ValueCountFrequency (%)
E 134
 
8.0%
130
 
7.8%
I 127
 
7.6%
N 112
 
6.7%
R 110
 
6.6%
T 109
 
6.5%
A 102
 
6.1%
S 97
 
5.8%
O 81
 
4.9%
C 75
 
4.5%
Other values (47) 588
35.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1342
80.6%
Lowercase Letter 136
 
8.2%
Space Separator 130
 
7.8%
Other Punctuation 32
 
1.9%
Close Punctuation 10
 
0.6%
Open Punctuation 10
 
0.6%
Dash Punctuation 5
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 134
10.0%
I 127
 
9.5%
N 112
 
8.3%
R 110
 
8.2%
T 109
 
8.1%
A 102
 
7.6%
S 97
 
7.2%
O 81
 
6.0%
C 75
 
5.6%
L 62
 
4.6%
Other values (15) 333
24.8%
Lowercase Letter
ValueCountFrequency (%)
n 19
14.0%
s 14
10.3%
i 13
9.6%
e 11
8.1%
u 11
8.1%
t 10
 
7.4%
r 10
 
7.4%
o 9
 
6.6%
c 7
 
5.1%
a 6
 
4.4%
Other values (13) 26
19.1%
Other Punctuation
ValueCountFrequency (%)
, 16
50.0%
& 12
37.5%
. 2
 
6.2%
: 1
 
3.1%
/ 1
 
3.1%
Space Separator
ValueCountFrequency (%)
130
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1478
88.8%
Common 187
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 134
 
9.1%
I 127
 
8.6%
N 112
 
7.6%
R 110
 
7.4%
T 109
 
7.4%
A 102
 
6.9%
S 97
 
6.6%
O 81
 
5.5%
C 75
 
5.1%
L 62
 
4.2%
Other values (38) 469
31.7%
Common
ValueCountFrequency (%)
130
69.5%
, 16
 
8.6%
& 12
 
6.4%
) 10
 
5.3%
( 10
 
5.3%
- 5
 
2.7%
. 2
 
1.1%
: 1
 
0.5%
/ 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 134
 
8.0%
130
 
7.8%
I 127
 
7.6%
N 112
 
6.7%
R 110
 
6.6%
T 109
 
6.5%
A 102
 
6.1%
S 97
 
5.8%
O 81
 
4.9%
C 75
 
4.5%
Other values (47) 588
35.3%

2000-01
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.757302
Minimum0
Maximum832.07
Zeros23
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:47.019247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.03
Q323.51
95-th percentile171.035
Maximum832.07
Range832.07
Interquartile range (IQR)23.51

Descriptive statistics

Standard deviation112.22786
Coefficient of variation (CV)2.9723485
Kurtosis41.709285
Mean37.757302
Median Absolute Deviation (MAD)4.03
Skewness6.052167
Sum2378.71
Variance12595.093
MonotonicityNot monotonic
2024-03-14T11:38:47.162216image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 23
36.5%
22.69 1
 
1.6%
67.72 1
 
1.6%
2.06 1
 
1.6%
60.04 1
 
1.6%
16.02 1
 
1.6%
45.75 1
 
1.6%
0.1 1
 
1.6%
9.75 1
 
1.6%
33.87 1
 
1.6%
Other values (31) 31
49.2%
ValueCountFrequency (%)
0 23
36.5%
0.1 1
 
1.6%
1.01 1
 
1.6%
1.05 1
 
1.6%
1.32 1
 
1.6%
1.42 1
 
1.6%
2.06 1
 
1.6%
2.41 1
 
1.6%
3.64 1
 
1.6%
4.03 1
 
1.6%
ValueCountFrequency (%)
832.07 1
1.6%
228.39 1
1.6%
195.33 1
1.6%
177.69 1
1.6%
111.14 1
1.6%
89.42 1
1.6%
81.5 1
1.6%
79.76 1
1.6%
71.38 1
1.6%
67.72 1
1.6%

2001-02
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.931587
Minimum0
Maximum873.23
Zeros18
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:47.316215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.07
Q344.83
95-th percentile234.321
Maximum873.23
Range873.23
Interquartile range (IQR)44.83

Descriptive statistics

Standard deviation157.87874
Coefficient of variation (CV)2.469495
Kurtosis16.287041
Mean63.931587
Median Absolute Deviation (MAD)5.07
Skewness3.8818679
Sum4027.69
Variance24925.695
MonotonicityNot monotonic
2024-03-14T11:38:47.469251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 18
28.6%
14.14 1
 
1.6%
0.05 1
 
1.6%
16.7 1
 
1.6%
11.04 1
 
1.6%
219.39 1
 
1.6%
46.39 1
 
1.6%
0.2 1
 
1.6%
0.94 1
 
1.6%
8.37 1
 
1.6%
Other values (36) 36
57.1%
ValueCountFrequency (%)
0 18
28.6%
0.05 1
 
1.6%
0.11 1
 
1.6%
0.14 1
 
1.6%
0.18 1
 
1.6%
0.2 1
 
1.6%
0.36 1
 
1.6%
0.78 1
 
1.6%
0.94 1
 
1.6%
1.04 1
 
1.6%
ValueCountFrequency (%)
873.23 1
1.6%
757.44 1
1.6%
419.39 1
1.6%
235.76 1
1.6%
221.37 1
1.6%
219.39 1
1.6%
211.07 1
1.6%
187.95 1
1.6%
139.9 1
1.6%
87.23 1
1.6%

2002-03
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.925714
Minimum0
Maximum419.96
Zeros13
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:47.625212image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median11.01
Q336.555
95-th percentile288.168
Maximum419.96
Range419.96
Interquartile range (IQR)36.355

Descriptive statistics

Standard deviation86.606439
Coefficient of variation (CV)2.0175888
Kurtosis8.0415295
Mean42.925714
Median Absolute Deviation (MAD)11.01
Skewness2.8976557
Sum2704.32
Variance7500.6753
MonotonicityNot monotonic
2024-03-14T11:38:47.809250image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
20.6%
36.61 1
 
1.6%
0.21 1
 
1.6%
40.07 1
 
1.6%
54.18 1
 
1.6%
7.36 1
 
1.6%
3.97 1
 
1.6%
8.07 1
 
1.6%
36.88 1
 
1.6%
16.42 1
 
1.6%
Other values (41) 41
65.1%
ValueCountFrequency (%)
0 13
20.6%
0.01 1
 
1.6%
0.04 1
 
1.6%
0.19 1
 
1.6%
0.21 1
 
1.6%
0.56 1
 
1.6%
0.6 1
 
1.6%
1.3 1
 
1.6%
1.31 1
 
1.6%
1.7 1
 
1.6%
ValueCountFrequency (%)
419.96 1
1.6%
314.24 1
1.6%
296.34 1
1.6%
295.88 1
1.6%
218.76 1
1.6%
191.6 1
1.6%
128.12 1
1.6%
59.11 1
1.6%
56.78 1
1.6%
54.18 1
1.6%

2003-04
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.727778
Minimum0
Maximum368.32
Zeros10
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:47.966295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.215
median6.37
Q338.66
95-th percentile118.817
Maximum368.32
Range368.32
Interquartile range (IQR)38.445

Descriptive statistics

Standard deviation67.653735
Coefficient of variation (CV)1.9481159
Kurtosis11.567171
Mean34.727778
Median Absolute Deviation (MAD)6.37
Skewness3.1983231
Sum2187.85
Variance4577.0279
MonotonicityNot monotonic
2024-03-14T11:38:48.131264image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
15.9%
0.04 2
 
3.2%
8.11 1
 
1.6%
9.58 1
 
1.6%
108.91 1
 
1.6%
9.34 1
 
1.6%
7.15 1
 
1.6%
1.7 1
 
1.6%
109.22 1
 
1.6%
1.69 1
 
1.6%
Other values (43) 43
68.3%
ValueCountFrequency (%)
0 10
15.9%
0.01 1
 
1.6%
0.02 1
 
1.6%
0.04 2
 
3.2%
0.11 1
 
1.6%
0.19 1
 
1.6%
0.24 1
 
1.6%
0.3 1
 
1.6%
0.32 1
 
1.6%
0.43 1
 
1.6%
ValueCountFrequency (%)
368.32 1
1.6%
271.15 1
1.6%
235.48 1
1.6%
119.09 1
1.6%
116.36 1
1.6%
109.22 1
1.6%
108.91 1
1.6%
86.49 1
1.6%
82.31 1
1.6%
80.64 1
1.6%

2004-05
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.090317
Minimum0
Maximum527.9
Zeros6
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:48.300454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.715
median9.09
Q343.205
95-th percentile247.216
Maximum527.9
Range527.9
Interquartile range (IQR)42.49

Descriptive statistics

Standard deviation101.93487
Coefficient of variation (CV)1.9951896
Kurtosis10.779881
Mean51.090317
Median Absolute Deviation (MAD)9.03
Skewness3.133449
Sum3218.69
Variance10390.718
MonotonicityNot monotonic
2024-03-14T11:38:48.473966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
9.5%
200.38 1
 
1.6%
0.05 1
 
1.6%
2.7 1
 
1.6%
2.94 1
 
1.6%
139 1
 
1.6%
43.98 1
 
1.6%
9.09 1
 
1.6%
0.89 1
 
1.6%
40.06 1
 
1.6%
Other values (48) 48
76.2%
ValueCountFrequency (%)
0 6
9.5%
0.01 1
 
1.6%
0.03 1
 
1.6%
0.05 1
 
1.6%
0.06 1
 
1.6%
0.07 1
 
1.6%
0.1 1
 
1.6%
0.16 1
 
1.6%
0.44 1
 
1.6%
0.47 1
 
1.6%
ValueCountFrequency (%)
527.9 1
1.6%
456.15 1
1.6%
293.36 1
1.6%
252.42 1
1.6%
200.38 1
1.6%
152.06 1
1.6%
139 1
1.6%
121.97 1
1.6%
121.83 1
1.6%
118.33 1
1.6%

2005-06
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.93254
Minimum0
Maximum1359.97
Zeros7
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:48.635001image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.23
median22.62
Q363.855
95-th percentile445.644
Maximum1359.97
Range1359.97
Interquartile range (IQR)62.625

Descriptive statistics

Standard deviation206.43697
Coefficient of variation (CV)2.3476743
Kurtosis23.950437
Mean87.93254
Median Absolute Deviation (MAD)22.12
Skewness4.4761071
Sum5539.75
Variance42616.221
MonotonicityNot monotonic
2024-03-14T11:38:48.807966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
11.1%
149.13 1
 
1.6%
47.4 1
 
1.6%
27.38 1
 
1.6%
3 1
 
1.6%
169.83 1
 
1.6%
41.74 1
 
1.6%
12.31 1
 
1.6%
87.42 1
 
1.6%
34.09 1
 
1.6%
Other values (47) 47
74.6%
ValueCountFrequency (%)
0 7
11.1%
0.1 1
 
1.6%
0.33 1
 
1.6%
0.38 1
 
1.6%
0.5 1
 
1.6%
0.59 1
 
1.6%
0.74 1
 
1.6%
0.81 1
 
1.6%
0.93 1
 
1.6%
1.11 1
 
1.6%
ValueCountFrequency (%)
1359.97 1
1.6%
617.98 1
1.6%
548.61 1
1.6%
452.08 1
1.6%
387.72 1
1.6%
228.71 1
1.6%
172.44 1
1.6%
169.83 1
1.6%
164.76 1
1.6%
149.13 1
1.6%

2006-07
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.2819
Minimum0
Maximum4713.78
Zeros8
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:48.976969image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.16
median25.82
Q3108.325
95-th percentile459.346
Maximum4713.78
Range4713.78
Interquartile range (IQR)104.165

Descriptive statistics

Standard deviation686.78312
Coefficient of variation (CV)3.4636702
Kurtosis32.937355
Mean198.2819
Median Absolute Deviation (MAD)25.82
Skewness5.5338261
Sum12491.76
Variance471671.05
MonotonicityNot monotonic
2024-03-14T11:38:49.136967image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
12.7%
169.94 1
 
1.6%
224.2 1
 
1.6%
5.08 1
 
1.6%
9.85 1
 
1.6%
27.58 1
 
1.6%
102 1
 
1.6%
16.22 1
 
1.6%
6.04 1
 
1.6%
18.75 1
 
1.6%
Other values (46) 46
73.0%
ValueCountFrequency (%)
0 8
12.7%
0.04 1
 
1.6%
0.07 1
 
1.6%
0.99 1
 
1.6%
1.3 1
 
1.6%
1.43 1
 
1.6%
2.44 1
 
1.6%
2.81 1
 
1.6%
3.31 1
 
1.6%
5.01 1
 
1.6%
ValueCountFrequency (%)
4713.78 1
1.6%
2613.33 1
1.6%
1392.95 1
1.6%
476.51 1
1.6%
304.87 1
1.6%
260.72 1
1.6%
242.47 1
1.6%
224.2 1
1.6%
195.66 1
1.6%
169.94 1
1.6%

2007-08
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean390.08571
Minimum0
Maximum6986.17
Zeros2
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:49.293971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.478
Q19.95
median58.82
Q3279.27
95-th percentile1370.095
Maximum6986.17
Range6986.17
Interquartile range (IQR)269.32

Descriptive statistics

Standard deviation1026.2499
Coefficient of variation (CV)2.6308319
Kurtosis29.631477
Mean390.08571
Median Absolute Deviation (MAD)57.55
Skewness5.1077242
Sum24575.4
Variance1053188.9
MonotonicityNot monotonic
2024-03-14T11:38:49.603004image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.27 2
 
3.2%
0 2
 
3.2%
1175.75 1
 
1.6%
16.89 1
 
1.6%
31.24 1
 
1.6%
10.07 1
 
1.6%
270.05 1
 
1.6%
70.17 1
 
1.6%
1.53 1
 
1.6%
6.38 1
 
1.6%
Other values (51) 51
81.0%
ValueCountFrequency (%)
0 2
3.2%
0.01 1
1.6%
0.39 1
1.6%
1.27 2
3.2%
1.51 1
1.6%
1.53 1
1.6%
1.95 1
1.6%
2.23 1
1.6%
2.28 1
1.6%
5.51 1
1.6%
ValueCountFrequency (%)
6986.17 1
1.6%
3887.33 1
1.6%
1405.04 1
1.6%
1382.25 1
1.6%
1260.7 1
1.6%
1175.75 1
1.6%
988.68 1
1.6%
918.18 1
1.6%
656.1 1
1.6%
653.74 1
1.6%

2008-09
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean498.34857
Minimum0
Maximum6183.49
Zeros5
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:49.769022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.95
median84.88
Q3383.32
95-th percentile2448.737
Maximum6183.49
Range6183.49
Interquartile range (IQR)371.37

Descriptive statistics

Standard deviation1134.649
Coefficient of variation (CV)2.2768181
Kurtosis13.609974
Mean498.34857
Median Absolute Deviation (MAD)83.83
Skewness3.6009337
Sum31395.96
Variance1287428.4
MonotonicityNot monotonic
2024-03-14T11:38:49.925059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
7.9%
959.94 1
 
1.6%
11.27 1
 
1.6%
157.52 1
 
1.6%
272.51 1
 
1.6%
5.01 1
 
1.6%
144.7 1
 
1.6%
102.71 1
 
1.6%
42.88 1
 
1.6%
22.03 1
 
1.6%
Other values (49) 49
77.8%
ValueCountFrequency (%)
0 5
7.9%
0.09 1
 
1.6%
0.22 1
 
1.6%
0.83 1
 
1.6%
1.05 1
 
1.6%
1.17 1
 
1.6%
2.27 1
 
1.6%
3.32 1
 
1.6%
5.01 1
 
1.6%
5.35 1
 
1.6%
ValueCountFrequency (%)
6183.49 1
1.6%
4657.51 1
1.6%
4246.76 1
1.6%
2548.63 1
1.6%
1549.7 1
1.6%
1543.34 1
1.6%
1150.03 1
1.6%
959.94 1
1.6%
907.66 1
1.6%
735.04 1
1.6%

2009-10
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410.06952
Minimum0
Maximum5466.13
Zeros5
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:50.090053image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.88
median69.74
Q3341.595
95-th percentile1268.24
Maximum5466.13
Range5466.13
Interquartile range (IQR)333.715

Descriptive statistics

Standard deviation926.81463
Coefficient of variation (CV)2.2601402
Kurtosis17.981421
Mean410.06952
Median Absolute Deviation (MAD)69.64
Skewness4.0264878
Sum25834.38
Variance858985.35
MonotonicityNot monotonic
2024-03-14T11:38:50.243020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
7.9%
419.88 1
 
1.6%
341.31 1
 
1.6%
0.1 1
 
1.6%
112.02 1
 
1.6%
278.89 1
 
1.6%
69.74 1
 
1.6%
24.58 1
 
1.6%
24.12 1
 
1.6%
5.06 1
 
1.6%
Other values (49) 49
77.8%
ValueCountFrequency (%)
0 5
7.9%
0.1 1
 
1.6%
0.25 1
 
1.6%
0.27 1
 
1.6%
1.88 1
 
1.6%
2.83 1
 
1.6%
3.96 1
 
1.6%
4.02 1
 
1.6%
5.06 1
 
1.6%
6.54 1
 
1.6%
ValueCountFrequency (%)
5466.13 1
1.6%
4174.53 1
1.6%
2539.26 1
1.6%
1271.79 1
1.6%
1236.29 1
1.6%
1222.22 1
1.6%
1147.56 1
1.6%
871.86 1
1.6%
753.02 1
1.6%
737.95 1
1.6%

2010-11
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean339.41381
Minimum0
Maximum3296.09
Zeros3
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:50.409055image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.019
Q18.43
median58.07
Q3304.28
95-th percentile1644.324
Maximum3296.09
Range3296.09
Interquartile range (IQR)295.85

Descriptive statistics

Standard deviation627.14114
Coefficient of variation (CV)1.8477184
Kurtosis8.693432
Mean339.41381
Median Absolute Deviation (MAD)57.9
Skewness2.7834141
Sum21383.07
Variance393306.01
MonotonicityNot monotonic
2024-03-14T11:38:50.567019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
4.8%
1098.14 1
 
1.6%
274.84 1
 
1.6%
6.53 1
 
1.6%
0.17 1
 
1.6%
57.71 1
 
1.6%
188.67 1
 
1.6%
58.07 1
 
1.6%
102.9 1
 
1.6%
17.21 1
 
1.6%
Other values (51) 51
81.0%
ValueCountFrequency (%)
0 3
4.8%
0.01 1
 
1.6%
0.1 1
 
1.6%
0.17 1
 
1.6%
0.49 1
 
1.6%
0.63 1
 
1.6%
0.81 1
 
1.6%
1.58 1
 
1.6%
1.77 1
 
1.6%
2.49 1
 
1.6%
ValueCountFrequency (%)
3296.09 1
1.6%
2354.4 1
1.6%
1664.5 1
1.6%
1663.03 1
1.6%
1475.97 1
1.6%
1299.41 1
1.6%
1271.77 1
1.6%
1098.14 1
1.6%
779.81 1
1.6%
675.07 1
1.6%

2011-12
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean557.4727
Minimum0
Maximum5215.98
Zeros4
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:50.733019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.055
Q122.72
median129.36
Q3593.525
95-th percentile3029.7
Maximum5215.98
Range5215.98
Interquartile range (IQR)570.805

Descriptive statistics

Standard deviation1031.4741
Coefficient of variation (CV)1.8502683
Kurtosis8.3029678
Mean557.4727
Median Absolute Deviation (MAD)125.7
Skewness2.7992151
Sum35120.78
Variance1063938.7
MonotonicityNot monotonic
2024-03-14T11:38:50.901060image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
6.3%
1786.14 1
 
1.6%
289.89 1
 
1.6%
69.7 1
 
1.6%
170.21 1
 
1.6%
65.02 1
 
1.6%
222.08 1
 
1.6%
187.37 1
 
1.6%
8.3 1
 
1.6%
5.84 1
 
1.6%
Other values (50) 50
79.4%
ValueCountFrequency (%)
0 4
6.3%
0.55 1
 
1.6%
0.58 1
 
1.6%
2.77 1
 
1.6%
3.66 1
 
1.6%
3.99 1
 
1.6%
4.44 1
 
1.6%
5.32 1
 
1.6%
5.84 1
 
1.6%
7.08 1
 
1.6%
ValueCountFrequency (%)
5215.98 1
1.6%
4040.71 1
1.6%
3232.28 1
1.6%
3140.78 1
1.6%
2029.98 1
1.6%
1997.24 1
1.6%
1786.14 1
1.6%
1652.38 1
1.6%
1295.34 1
1.6%
992.86 1
1.6%

2012-13
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean355.93
Minimum0
Maximum4832.98
Zeros5
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:51.059063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.115
median95.41
Q3288.025
95-th percentile1452.856
Maximum4832.98
Range4832.98
Interquartile range (IQR)272.91

Descriptive statistics

Standard deviation778.09137
Coefficient of variation (CV)2.1860798
Kurtosis20.034167
Mean355.93
Median Absolute Deviation (MAD)91.08
Skewness4.1665736
Sum22423.59
Variance605426.18
MonotonicityNot monotonic
2024-03-14T11:38:51.229032image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
7.9%
1466.23 1
 
1.6%
142.32 1
 
1.6%
12.26 1
 
1.6%
107.21 1
 
1.6%
401.46 1
 
1.6%
108.39 1
 
1.6%
160.07 1
 
1.6%
642.18 1
 
1.6%
46.7 1
 
1.6%
Other values (49) 49
77.8%
ValueCountFrequency (%)
0 5
7.9%
0.15 1
 
1.6%
0.27 1
 
1.6%
0.41 1
 
1.6%
0.58 1
 
1.6%
4.33 1
 
1.6%
5.09 1
 
1.6%
5.1 1
 
1.6%
6.71 1
 
1.6%
12.26 1
 
1.6%
ValueCountFrequency (%)
4832.98 1
1.6%
3259.05 1
1.6%
1537.28 1
1.6%
1466.23 1
1.6%
1332.49 1
1.6%
1123.46 1
1.6%
1106.52 1
1.6%
717.8 1
1.6%
642.18 1
1.6%
535.68 1
1.6%

2013-14
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean385.70349
Minimum0
Maximum3982.89
Zeros3
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:51.396022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.184
Q116.61
median113.78
Q3473.06
95-th percentile1339.746
Maximum3982.89
Range3982.89
Interquartile range (IQR)456.45

Descriptive statistics

Standard deviation658.42994
Coefficient of variation (CV)1.7070884
Kurtosis14.279305
Mean385.70349
Median Absolute Deviation (MAD)113.24
Skewness3.2979528
Sum24299.32
Variance433529.99
MonotonicityNot monotonic
2024-03-14T11:38:51.566022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
4.8%
567.63 1
 
1.6%
0.82 1
 
1.6%
3.08 1
 
1.6%
814.58 1
 
1.6%
3982.89 1
 
1.6%
21.55 1
 
1.6%
108.44 1
 
1.6%
370.54 1
 
1.6%
6.14 1
 
1.6%
Other values (51) 51
81.0%
ValueCountFrequency (%)
0 3
4.8%
0.17 1
 
1.6%
0.31 1
 
1.6%
0.53 1
 
1.6%
0.54 1
 
1.6%
0.82 1
 
1.6%
0.97 1
 
1.6%
2.96 1
 
1.6%
3.08 1
 
1.6%
5.86 1
 
1.6%
ValueCountFrequency (%)
3982.89 1
1.6%
2225.1 1
1.6%
1517.28 1
1.6%
1343.39 1
1.6%
1306.95 1
1.6%
1279.34 1
1.6%
1226.05 1
1.6%
1126.27 1
1.6%
1066.08 1
1.6%
814.58 1
1.6%

2014-15
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean490.95984
Minimum0
Maximum4443.26
Zeros2
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:51.740078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.76
Q133.8
median177.22
Q3595.39
95-th percentile2682.68
Maximum4443.26
Range4443.26
Interquartile range (IQR)561.59

Descriptive statistics

Standard deviation837.78706
Coefficient of variation (CV)1.7064269
Kurtosis9.1307656
Mean490.95984
Median Absolute Deviation (MAD)156
Skewness2.8976347
Sum30930.47
Variance701887.16
MonotonicityNot monotonic
2024-03-14T11:38:51.897110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
3.2%
359.34 1
 
1.6%
458.13 1
 
1.6%
27.77 1
 
1.6%
225.38 1
 
1.6%
515.86 1
 
1.6%
148.34 1
 
1.6%
177.22 1
 
1.6%
284.51 1
 
1.6%
34.21 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
0 2
3.2%
0.08 1
1.6%
0.75 1
1.6%
0.85 1
1.6%
1.33 1
1.6%
1.36 1
1.6%
1.43 1
1.6%
1.9 1
1.6%
8.97 1
1.6%
21.44 1
1.6%
ValueCountFrequency (%)
4443.26 1
1.6%
2894.94 1
1.6%
2727.96 1
1.6%
2725.64 1
1.6%
2296.04 1
1.6%
1497.74 1
1.6%
1079.02 1
1.6%
870.25 1
1.6%
777.01 1
1.6%
769.14 1
1.6%

2015-16
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean634.93635
Minimum0
Maximum6889.46
Zeros5
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:52.053074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median159.13
Q3519.07
95-th percentile3713.47
Maximum6889.46
Range6889.46
Interquartile range (IQR)489.07

Descriptive statistics

Standard deviation1335.3077
Coefficient of variation (CV)2.1030576
Kurtosis11.666251
Mean634.93635
Median Absolute Deviation (MAD)152.77
Skewness3.3799707
Sum40000.99
Variance1783046.7
MonotonicityNot monotonic
2024-03-14T11:38:52.217081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
7.9%
456.31 1
 
1.6%
0.1 1
 
1.6%
105.85 1
 
1.6%
202.36 1
 
1.6%
505.88 1
 
1.6%
34.22 1
 
1.6%
193.26 1
 
1.6%
296.15 1
 
1.6%
17.13 1
 
1.6%
Other values (49) 49
77.8%
ValueCountFrequency (%)
0 5
7.9%
0.1 1
 
1.6%
0.82 1
 
1.6%
1.12 1
 
1.6%
3.32 1
 
1.6%
6.36 1
 
1.6%
7.42 1
 
1.6%
16.44 1
 
1.6%
17.13 1
 
1.6%
19.69 1
 
1.6%
ValueCountFrequency (%)
6889.46 1
1.6%
5904.36 1
1.6%
4510.71 1
1.6%
3845.32 1
1.6%
2526.82 1
1.6%
1469.95 1
1.6%
1332.69 1
1.6%
1324.4 1
1.6%
1009.34 1
1.6%
868.8 1
1.6%

2016-17
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean690.13111
Minimum0
Maximum8684.07
Zeros6
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size632.0 B
2024-03-14T11:38:52.389188image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119.905
median110.86
Q3741.22
95-th percentile2327.629
Maximum8684.07
Range8684.07
Interquartile range (IQR)721.315

Descriptive statistics

Standard deviation1411.9654
Coefficient of variation (CV)2.0459378
Kurtosis18.092572
Mean690.13111
Median Absolute Deviation (MAD)110.86
Skewness3.8913695
Sum43478.26
Variance1993646.2
MonotonicityNot monotonic
2024-03-14T11:38:52.548221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
9.5%
1440.18 1
 
1.6%
261.14 1
 
1.6%
15.92 1
 
1.6%
110.86 1
 
1.6%
727.22 1
 
1.6%
108.45 1
 
1.6%
92.6 1
 
1.6%
262.76 1
 
1.6%
2.3 1
 
1.6%
Other values (48) 48
76.2%
ValueCountFrequency (%)
0 6
9.5%
0.8 1
 
1.6%
0.89 1
 
1.6%
1.6 1
 
1.6%
2.3 1
 
1.6%
7.44 1
 
1.6%
10.23 1
 
1.6%
10.7 1
 
1.6%
15.19 1
 
1.6%
15.4 1
 
1.6%
ValueCountFrequency (%)
8684.07 1
1.6%
5563.69 1
1.6%
3651.71 1
1.6%
2338.4 1
1.6%
2230.69 1
1.6%
2130.1 1
1.6%
1860.73 1
1.6%
1609.32 1
1.6%
1516.68 1
1.6%
1440.18 1
1.6%

Interactions

2024-03-14T11:38:44.121659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:11.151872image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:13.853436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:15.915143image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:17.936388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:19.956150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:21.998574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-03-14T11:38:19.372104image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:21.439605image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:23.418301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:25.290567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:27.396955image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:29.410205image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:31.368389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:33.568021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:35.573942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:37.523976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:39.676742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:41.552935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:43.530443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:45.675351image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:13.143221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:15.415484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:17.494170image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:19.488105image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:21.548571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:23.531301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:25.394571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:27.508919image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:29.525168image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:31.478389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:33.683089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:35.694942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:37.629986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:39.791739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:41.658058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:43.644408image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:45.796348image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:13.314222image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:15.547487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:17.614119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:19.615145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:21.671605image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:23.660269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:25.514611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:27.635916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:29.651165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:31.609390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:33.835088image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:35.826944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:37.754021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:39.919774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:41.777019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:43.772615image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:45.898386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:13.598436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:15.657140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:17.718118image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:19.726104image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:21.775580image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:23.767446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:25.613569image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:27.743917image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:29.759165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:31.740432image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:33.944087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:35.930941image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:37.857986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:40.030775image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:41.876019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:43.882614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:46.014347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:13.733436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:15.795141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:17.833380image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:19.848107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:21.896605image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:23.890403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:25.728696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:27.869072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:29.877298image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:31.857419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:34.065124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:36.049944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:37.979986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:40.157864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:41.990019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-03-14T11:38:44.006612image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2024-03-14T11:38:52.707970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2000-012001-022002-032003-042004-052005-062006-072007-082008-092009-102010-112011-122012-132013-142014-152015-162016-17Sector
2000-011.0000.8380.7300.6830.6060.6720.6770.5910.6600.6030.5750.6250.5240.5880.5580.4520.5541.000
2001-020.8381.0000.8500.8070.7310.6870.6790.7010.7230.6880.6450.6810.5660.6380.6500.5290.6111.000
2002-030.7300.8501.0000.8090.7730.7460.7250.7350.7230.6930.6700.7200.6240.6810.6720.6300.6211.000
2003-040.6830.8070.8091.0000.7930.6700.6770.7260.7470.6720.6090.6100.5630.5750.6370.5150.4831.000
2004-050.6060.7310.7730.7931.0000.6940.7500.7780.7710.7150.6840.6890.6300.6650.6870.6030.5331.000
2005-060.6720.6870.7460.6700.6941.0000.8240.6840.7400.6890.7180.7270.6280.7630.6900.6280.6481.000
2006-070.6770.6790.7250.6770.7500.8241.0000.7880.8430.7660.8360.8030.7010.8340.7870.7190.7491.000
2007-080.5910.7010.7350.7260.7780.6840.7881.0000.8450.8240.7800.7440.6270.7070.7520.7080.6371.000
2008-090.6600.7230.7230.7470.7710.7400.8430.8451.0000.7970.8350.8340.6950.8040.8030.7000.7371.000
2009-100.6030.6880.6930.6720.7150.6890.7660.8240.7971.0000.8970.8550.7990.8000.8460.8220.7771.000
2010-110.5750.6450.6700.6090.6840.7180.8360.7800.8350.8971.0000.8920.7870.8330.9000.8260.8391.000
2011-120.6250.6810.7200.6100.6890.7270.8030.7440.8340.8550.8921.0000.8480.8510.8960.8390.8561.000
2012-130.5240.5660.6240.5630.6300.6280.7010.6270.6950.7990.7870.8481.0000.8630.8470.8120.7711.000
2013-140.5880.6380.6810.5750.6650.7630.8340.7070.8040.8000.8330.8510.8631.0000.8430.8230.8211.000
2014-150.5580.6500.6720.6370.6870.6900.7870.7520.8030.8460.9000.8960.8470.8431.0000.8600.8631.000
2015-160.4520.5290.6300.5150.6030.6280.7190.7080.7000.8220.8260.8390.8120.8230.8601.0000.8571.000
2016-170.5540.6110.6210.4830.5330.6480.7490.6370.7370.7770.8390.8560.7710.8210.8630.8571.0001.000
Sector1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-03-14T11:38:46.193369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:38:46.522350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Sector2000-012001-022002-032003-042004-052005-062006-072007-082008-092009-102010-112011-122012-132013-142014-152015-162016-17
0METALLURGICAL INDUSTRIES22.6914.1436.618.11200.38149.13169.941175.75959.94419.881098.141786.141466.23567.63359.34456.311440.18
1MINING1.326.5210.0623.489.927.406.62444.3634.16174.4079.51142.6557.8912.73684.39520.6755.75
2POWER89.42757.4459.1127.0943.3772.69157.15988.68907.661271.791271.771652.38535.681066.08707.04868.801112.98
3NON-CONVENTIONAL ENERGY0.000.001.704.141.271.352.4458.82125.88622.52214.40452.171106.52414.25615.95776.51783.57
4COAL PRODUCTION0.000.000.000.040.009.141.3014.080.220.000.000.000.002.960.000.000.00
5PETROLEUM & NATURAL GAS9.35211.0756.7880.64102.7812.0987.711405.04349.29265.53556.432029.98214.80112.231079.02103.02180.40
6BOILERS AND STEAM GENERATING PLANTS0.000.000.000.040.540.003.311.510.003.960.6331.7920.050.171.3377.9153.91
7PRIME MOVER (OTHER THAN ELECTRICAL GENERATORS)0.000.000.000.002.660.7425.5740.5374.8839.50166.44313.75184.60212.78230.70159.13286.88
8ELECTRICAL EQUIPMENTS79.7665.7634.7173.2097.4039.5076.85653.74417.35728.27153.90566.39195.87134.31574.83444.882230.69
9COMPUTER SOFTWARE & HARDWARE228.39419.39314.24368.32527.901359.972613.331382.251543.34871.86779.81796.35485.961126.272296.045904.363651.71
Sector2000-012001-022002-032003-042004-052005-062006-072007-082008-092009-102010-112011-122012-132013-142014-152015-162016-17
53TRADING11.4943.2738.1331.1214.2228.93114.65345.02643.64737.95498.04731.55717.801343.392727.963845.322338.40
54RETAIL TRADING0.000.000.000.000.000.000.001.270.0913.7326.2731.7022.3111.30168.72262.24450.94
55AGRICULTURE SERVICES17.5214.0611.010.593.839.0812.5358.135.351222.2243.9049.02161.4791.0159.9584.6576.43
56DIAMOND,GOLD ORNAMENTS18.830.361.301.968.5815.5261.9759.1583.5031.0819.5936.3052.6142.56280.1858.54123.92
57TEA AND COFFEE (PROCESSING & WAREHOUSING COFFEE & RUBBER)20.230.140.000.320.011.436.2018.9437.088.153.125.320.275.861.431.121.60
58PRINTING OF BOOKS (INCLUDING LITHO PRINTING INDUSTRY)0.000.006.300.000.069.9020.0435.5431.6170.5136.6347.3914.34113.7872.58122.8153.17
59COIR0.000.000.000.000.470.590.040.010.000.250.100.550.150.541.360.000.00
60CONSTRUCTION (INFRASTRUCTURE) ACTIVITIES0.000.000.000.000.000.9364.06182.92172.70324.56675.07386.28283.89485.37870.254510.711860.73
61CONSTRUCTION DEVELOPMENT: Townships, housing, built-up infrastructure and construction-development projects24.3351.7536.1047.04152.06228.711392.953887.334657.515466.131663.033140.781332.491226.05769.14112.55105.14
62MISCELLANEOUS INDUSTRIES832.07221.37218.76235.48121.83164.76304.87528.421549.701147.561475.97813.38229.49468.74765.88668.77296.40